Automatic correlation of test logs with service ticket

Abstract

Automatic correlation of test logs with service ticket (e.g., using a computerized tool), is enabled. For example, a system can comprise: a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising, based on test log data representative of test logs and using machine learning attention, generating a test log output vector, based on the test log output vector and a service ticket output vector, determining a probability of a relation between a test log represented in the test log output vector and a service ticket represented in the service ticket output vector, and in response to the probability being determined to satisfy a threshold relation probability, marking the test log and the service ticket as related.

What is claimed is: 
     
         1 . A system, comprising:
 a processor; and   a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:   based on test log data representative of test logs and using machine learning attention, generating a test log output vector;   based on the test log output vector and a service ticket output vector, determining a probability of a relation between a test log represented in the test log output vector and a service ticket represented in the service ticket output vector; and   in response to the probability being determined to satisfy a threshold relation probability, marking the test log and the service ticket as related.   
     
     
         2 . The system of  claim 1 , wherein the generating of the test log output vector further comprises generating the test log output vector using a test log neural network applied to the test log data. 
     
     
         3 . The system of  claim 2 , wherein the test log neural network comprises a rectified linear unit activation function. 
     
     
         4 . The system of  claim 1 , wherein the service ticket output vector is generated using a service ticket neural network applied to service ticket data representative of service tickets. 
     
     
         5 . The system of  claim 4 , wherein the service ticket data comprises a service ticket sparse matrix. 
     
     
         6 . The system of  claim 4 , wherein the service ticket neural network comprises a rectified linear unit activation function. 
     
     
         7 . The system of  claim 1 , wherein the test log data comprises a test log sparse matrix. 
     
     
         8 . The system of  claim 1 , wherein the determining of the probability of the relation between the test log and the service ticket comprises:
 concatenating the test log output vector and the service ticket output vector, resulting in a concatenated relation vector, and   inputting the concatenated relation vector to a relation neural network.   
     
     
         9 . The system of  claim 8 , wherein the relation neural network comprises a softmax activation function. 
     
     
         10 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
 based on test log data representative of test logs and using machine learning attention, generating a test log output vector;   based on the test log output vector and a service ticket output vector, determining a probability of a relation between a test log represented in the test log output vector and a service ticket represented in the service ticket output vector; and   in response to the probability being determined not to satisfy a threshold relation probability, determining that the test log comprises a previously unidentified problem.   
     
     
         11 . The non-transitory machine-readable medium of  claim 10 , wherein the generating of the test log output vector comprises generating the test log output vector using a test log neural network applied to the test log data. 
     
     
         12 . The non-transitory machine-readable medium of  claim 11 , wherein the test log neural network comprises a rectified linear unit activation function. 
     
     
         13 . The non-transitory machine-readable medium of  claim 10 , wherein the service ticket output vector is generated using a service ticket neural network applied to service ticket data representative of service tickets. 
     
     
         14 . The non-transitory machine-readable medium of  claim 13 , wherein the service ticket data comprises a service ticket sparse matrix. 
     
     
         15 . The non-transitory machine-readable medium of  claim 13 , wherein the service ticket neural network comprises a rectified linear unit activation function. 
     
     
         16 . A method, comprising:
 based on test log data representative of test logs and using machine learning attention, generating, by a system comprising a processor, a test log output vector; and   based on the test log output vector and a service ticket output vector, determining, by the system, a probability that a test log represented in the test log output vector is related to a service ticket represented in the service ticket output vector.   
     
     
         17 . The method of  claim 16 , further comprising:
 generating, by the system, an output representative of the probability.   
     
     
         18 . The method of  claim 16 , wherein the determining of the probability that the test log is related to the service ticket comprises:
 concatenating the test log output vector and the service ticket output vector, resulting in a concatenated relation vector, and   subjecting the concatenated relation vector to a relation neural network.   
     
     
         19 . The method of  claim 18 , wherein the relation neural network comprises a softmax activation function. 
     
     
         20 . The method of  claim 16 , wherein the test log data comprises a test log sparse matrix.